The invention relates to the suppression of superimposed interferences in a measurement signal with a substantially periodic useful signal.
The measurement of signals may roughly be subdivided in general into a) the recognition of individual, more or less singular events and b) the monitoring of more or less frequently repetitive, substantially periodic signals. In either case, superimposed interferences limit the reliability of the measurement, and the object is to avoid, to suppress, or to filter out these interferences.
For the purposes of the present document, periodic signals should be understood to be those signals for which the useful signal has at least a periodic component, at least over a given domain of time, the frequency of which may indeed be time-dependent.
The recognition of the useful signal and the suppression of interferences is essential in particular in the field of medical patient monitoring, because interferences may lead to a false interpretation of the measurement values, or may render the measurement wholly useless.
A measurement which was found to be particularly sensitive to interfering influences is the pulsoximetrical determination of the oxygen content of the blood, because pulsoximetry is much more strongly influenced by movement artifacts than by the pulse signal determining the blood oxygen. Pulsoximetry involves the non-invasive, continuous determination of the oxygen content of the blood (oximetry), based on an analysis of the photospectrometrically measured pulse. It is necessary for this that a pulse curve (plethysmogram) is available at several wavelengths. In practice, almost all appliances operate with only two wavelengths, which renders it possible to achieve inexpensive, compact solutions. The principle of photometry is based on the fact that the quantity of absorbed light is determined by the degree of absorption of a substance and by the wavelength. Pulsoximeters utilize this effect in that the arterial blood volume, and exclusively the arterial blood volume, pulsates in the rhythm of the heartbeat. If a conclusion is to be drawn as to the value of the oxygen saturation from the obtained measurement data, a ratio of values is derived from the measurement data, which ratio then represents the oxygen saturation value. The basics and application possibilities of pulsoximetry are generally known and have frequently been described, in particular in EP-A-262778 (with a good summary of the theory), U.S. Pat. No. 4,167,331, or by Kxc3xa4stle et al. in xe2x80x9cA New Family of Sensors for Pulsoximetryxe2x80x9d, Hewlett-Packard Journal, vol. 48, no. 1, pp. 39-53, Feb. 1997
Methods proposed for recognizing and suppressing artifacts in pulsoximetrical measurements are in particular processes in the time domain, adaptive filters, spectral analyses, and methods in the time-frequency domain.
Among the methods in the time domain is the peak method, in which the basic signal is subdivided into the individual pulses, and the ratio value is determined from the extreme values of a pulse (cf. EP-A-102816 or U.S. Pat. No. 5,349,519). The essence of this artifact suppression is the comparison of properties such as amplitude, time between maximum and minimum, length, etc., of an identified pulse with those of a reference pulse which was derived from preceding pulses. A further method in the time domain is the ECG synchronization as described in U.S. Pat. No. 4,802,486. A temporal reference to the peripheral pulse is obtained here, derived from the R-spike in the ECG. In the split-wave method (cf. U.S. Pat. No. 5,386,026), the basic signal is scanned at equidistant intervals, independently of the pulse. Two scanning points are combined each time so as to obtain the ratio. Pulse-independent, continuous SpO2 values are thus created. A further method in the time domain for interference suppression is described in EP-A-870465. Here the basic signals are reduced to their AC components through constant average value subtraction. Interferences superimposed in the same direction on both basic signals can be eliminated through subtraction of the AC components.
A complex adaptive filter is described in WO 96/12435 and operates in accordance with the principle of echo suppression, known from telephony. A filter is controlled such that an image of the interference component is generated, which is subsequently subtracted from the interfered signal.
Spectral analyses for artifact recognition were published in a research paper by Rusch et al. in xe2x80x9cSignal Processing Method for Pulsoximetryxe2x80x9d, Comput. Biol. Med., vol. 26, no. 2, pp. 143-159, 1996, in which it is investigated whether it is possible to find the pulse frequency and amplitudes in a simpler manner in the frequency range. Several adjustments of the Fast Fourier transform (FFT) and the Discrete Cosine Transform (DCT) were compared. The algorithm proposed, however, has particular weaknesses in the suppression of movement artifacts.
Further methods of recognizing and suppressing artifacts are described in WO 97/00041, with the proposal to eliminate artifacts by simple mathematics, for which it is assumed that movement interferences lead to the same logarithmic changes in all wavelengths. In U.S. Pat. No. 5,588,429, the fractal dimension of the basic signals is determined, the fundamental idea being that the fractal dimension of a non-interfered signal is small, whereas that of an interfered signal is great. According to U.S. Pat. No. 5,355,882, only the instantaneous DC values are used within interfered intervals, whereas the AC values originate from moments before the interference.
Methods in the time-frequency domain for interference suppression with the use of so-called wavelet transforms are described inter alia in JP-A-10216096 (for living-body signals), U.S. Pat. No. 5,778,881 (for ECG applications), or EP-A-816863 (for radar applications).
U.S. Pat. No. 5,619,998 and U.S. Pat. No. 5,497,777 describe noise filter methods in the time-frequency domain for ultrasound imaging systems. The imaging signals are subdivided into overlapping sub-intervals of equal length. Each of the sub-intervals is transformed by means of a discrete wavelet technique. It is identified for each transformed sub-interval whether the wavelet transform coefficients relate to interferences or to the useful signal. The identification takes place here through the use of adaptive, non-linear threshold value formation. The wavelet coefficients which were selected as relating to the useful signal are retained, and those wavelets which were selected as belonging to interferences are erased. The remaining useful signal wavelet coefficients are transformed back in an inverse discrete wavelet transform.
Coifman et al. in xe2x80x9cExperiments with Adapted Wavelet De-Noising for Medical Signals and Imagesxe2x80x9d, published by Metin Akay in xe2x80x9cTime Frequency and Wavelets in Biomedical Signal Processingxe2x80x9d, IEEE, ISBN 0-7803-1147-7, 1997, pp. 323 ff., also describes an algorithm against interference in the time-frequency domain for medical signals and images. A one-dimensional signal such as, for example, a sound file, is subdivided into windows of a desired length. A wavelet packet transform with a number of filters is attempted in each window, the transform with the lowest entropy is retained as the best basis, and the coefficients are sorted in the order of falling amplitude. The coefficients having an amplitude smaller than a given energy threshold value are eliminated in each window, and a cost function for the coefficients (i.e. how many wavelet packet coefficients does it xe2x80x9ccostxe2x80x9d to achieve the energy for which all values  greater than  greater than 0 are counted) is evaluated repeatedly until the cost is greater than a given cost threshold value. The coefficients not considered (too small) are erased, and a new signal is reconstructed from the remaining coefficients.
It was found to be particularly unfavorable in the two methods in the time-frequency domain mentioned last that the useful signal may also become modified and distorted in the case of an erroneous assignment of the wavelet coefficients. This is in particular useless in the case of major interferences superimposed on the useful signal.
Each of the cited methods has its weak points in one or several applications. It has been found to be impossible until now to find a method for the suppression of interference which is fully satisfactory for all applications.
It is accordingly an object of the present invention to provide a further method for suppressing interferences superimposed on measurement signals with a substantially periodic useful signal. This object is achieved by means of the characteristic features of the independent claims. Advantageous embodiments are defined in the dependent claims.
The starting point for the present invention is the model that interferences superimposed on measurement signals with a substantially periodic nature are mostly of a transient nature. This means that the desired useful signals can be regarded as (substantially) periodic, and the interference signals superimposed thereon as (substantially) aperiodic. To simplify the discussion and to make it more understandable, the following text will refer only to periodic and aperiodic signals, also if this could relate to substantially periodic or substantially aperiodic signals, i.e. signals which have a predominantly periodic or aperiodic character, as applicable.
Whereas the periodic signals can be well represented as a sum of periodic basic functions through a suitable transformation, and often only few summation coefficients are sufficient for representing them, the aperiodic signals can be well represented through a summation of aperiodic basic functions, only few summation coefficients often being sufficient again for this representation. Conversely, however, the representation of the periodic signals through summation of aperiodic basic functions requires a high expenditure and a plurality of summation coefficients so as to represent these periodic signals satisfactorily. In other words, a periodic useful signal is distributed over a plurality of coefficients for summation of aperiodic basic functions, while the aperiodic interferences are usually distributed over only few summation coefficients of aperiodic basic functions, but with a higher amplitude. In a transformation of the measured signal into a summation of aperiodic basic functions, accordingly, few coefficients will often suffice, which are then to be suitably manipulated so as to suppress the interference of a transient nature.
According to the invention, an interference suppression takes place through transformation of the measurement signal into a summation of aperiodic basic functions. When it is recognized on the basis of the obtained coefficients of the summation that it probably relates to an interference, these coefficients will be suitably manipulated, and the summation thus manipulated is transformed back again. If no manipulation of the coefficients has taken place, a corresponding back-transformation can be omitted, and the measurement signal can be directly used.
A preferred transform is the so-called wavelet transform as described in particular in Mallat S. G., xe2x80x9cA Wavelet Tool of Signal Processingxe2x80x9d, Academic Press, San Diego, 1998, Wickerhauser M. V. xe2x80x9cAdaptive Wavelet Analysisxe2x80x9d, Vieweg and Sohn, Braunschweig, 1996, or von Daubechies I., xe2x80x9cTen Lectures on Waveletsxe2x80x9d, CBMS, vol. 61, SIAM Press Philadelphia, PA, 1992. A further discussion of the known wavelet transform may be omitted here, as a reference to the above and further background literature will suffice.
A suitable criterion for recognizing coefficients which are likely to have been influenced by interference and which can be ascribed to aperiodic signals such as interferences (also referred to as interference coefficients) is formed in particular by the amplitude or energy of the coefficients. If, for example, the coefficient amplitude (quantity) or the coefficient energy exceeds a given threshold, limit, or expectation value, which was preferably derived from preceding, uninterfered measuring phases or otherwise, for example empirically, the relevant coefficients will be regarded as such (interference) coefficients as point to the presence of an aperiodic interference. The expected interference component can be suppressed through a suitable manipulation of the coefficient amplitude such as, for example, reducing the amplitude to or towards the above or a further limit value (possibly derived from the first), however, without at the same time suppressing the useful signal.
This coefficient manipulation according to the invention differs significantly in particular from the one described by Coifinan et al. (see above). Whereas according to the invention only those coefficients are manipulated which are greater than a threshold value, Coifinan et al. manipulate only those which are smaller than a threshold value. The method described by Coifihan can be satisfactorily applied to interference suppression of signals having a signal-to-noise ratio S/N  greater than  greater than 1. The interference suppression according to the invention, by contrast, is found to be highly advantageous in particular for very high interference components with S/N  less than  less than 1.
It is thus possible for known periodic signals to suppress the interfering influence very strongly through the use of the coefficient manipulation according to the invention, given a knowledge of the useful signal. Since the measurement signal itself is always unknown in practice, however, and only a probable gradient can be assumed, care has definitely to be taken in the manipulation of the coefficients so as to keep an inadvertent elimination or inadvertent influencing of the useful signal small or to avoid it altogether. Overall, however, the coefficient manipulation according to the invention renders it possible, given a suitable choice and dimensioning of the decision criterion, to decide whether a xe2x80x9ctransientxe2x80x9d coefficient is present (i.e. a coefficient which derives substantially from a transient interferencexe2x80x94also called interference coefficient), so as to achieve an effective recognition and suppression of transient interferences with a suitable dimensioning of the correction of a recognized transient coefficient.
In a preferred embodiment, the decision criterion used for deciding whether a transient coefficient is present is an average value of the measurement signal. The average value is then preferably obtained through averaging of the measurement signal values over a time interval, which interval preferably extends some distance into the past. If the coefficient amplitude is greater than a given factor multiplied by the respective average value thus determined, this coefficient is regarded as a transient coefficient, and the amplitude is preferably reduced by a certain factor.
In another embodiment, the average value is obtained through averaging of the energy values of the measurement signal over a time interval, which interval extends some distance into the past. If the coefficient energy is greater than a given factor multiplied by the respective average value thus obtained, the respective coefficients are regarded as transient coefficients, and the amplitude is preferably reduced by a certain factor.
In a further embodiment, the decision criterion used for the recognition of transient coefficients is the xe2x80x9caveragexe2x80x9d energy of the measurement signal, preferably as the sum of the squared wavelet coefficients or the sum of the squared scanning values of the time signal in the time window considered. This xe2x80x9caveragexe2x80x9d energy is preferably determined such that a certain element is taken from the energy values (per wavelet band) of the preceding seconds (preferably 30 seconds) for each rank function. The rank function (rankxcex1) determines from a series of m values the n-smallest value; the parameter xcex1 (value preferably between 0 and 1) here lays down the rule n=xcex1xc2x7m. With a close to 0, an energy peak will arise which lies only little above the minimum. A favorable value for the application was found to be xcex1=0.2. In a preferred embodiment, a lower average energy threshold is then determined for each respective measurement value. When the coefficient energy is exceeded by a given factor multiplied by the respective lower average energy value, the trigger condition is fulfilled, and a manipulation of the excessive coefficient takes place.
The amplitude or energy gradient of the recorded measurement values up to the present moment is preferably taken for determining the decision criterion for recognizing a transient coefficient and/or for the manipulation of a recognized transient coefficient. Suitable wavelets were found to be in particular short wavelets such as, for example, Coiflet-2 or complex wavelets such as xe2x80x9cGabor-likexe2x80x9d wavelets (reference is made in particular to: Kingsbury N., xe2x80x9cA Complex Wavelet Transform with Perfect Reconstruction Using Low-Complexity Gabor-Like Filtersxe2x80x9d, IEEE Sig. Proc. Letters, September 1997, in this connection).
For determining the transient coefficients to be suppressed, preferably, the coefficients are first quantitatively sorted for each frequency band and, starting with the greatest coefficients, the cumulative energy of the frequency band is determined. Starting with the greatest coefficients, those coefficients of the cumulative energy which exceed a reference level are manipulated until the total of the cumulative energy exceeds the reference level.
If several measurement signals from one source are present, they are preferably forcibly synchronized, i.e. if a measurement signal is manipulated, the other one is correspondingly manipulated.
The interference suppression according to the invention renders it possible to act with precision exclusively on interfered signal portions, while non-interfered signal portions are not affected.
The interference suppression according to the invention is preferably used for removing noise from medical measurement signals. The invention is found to be particularly effective for pulsating medical measurement signals such as those which occur in particular in pulsoximetry or blood pressure measurements. The interference suppression according to the invention renders possible a reliable recognition and suppression of exactly those movement artifacts which are particularly strong interfering factors in pulsoximetry, because these movement artifacts are characterized by steep flanks and high amplitudes. Nevertheless, the interference suppression according to the invention is not limited to medical applications, but it may be used for measurements or signal recordings of any kind wherever aperiodic interferences may be superimposed on a periodic signal.